Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations5037
Missing cells1772
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory629.6 KiB
Average record size in memory128.0 B

Variable types

Numeric10
Text3
Categorical1
DateTime1

Alerts

id is highly overall correlated with number_of_reviewsHigh correlation
latitude is highly overall correlated with longitudeHigh correlation
longitude is highly overall correlated with latitudeHigh correlation
number_of_reviews is highly overall correlated with id and 1 other fieldsHigh correlation
reviews_per_month is highly overall correlated with number_of_reviewsHigh correlation
last_review has 886 (17.6%) missing values Missing
reviews_per_month has 886 (17.6%) missing values Missing
price is highly skewed (γ1 = 21.51400656) Skewed
id has unique values Unique
number_of_reviews has 886 (17.6%) zeros Zeros
availability_365 has 948 (18.8%) zeros Zeros

Reproduction

Analysis started2025-03-01 00:39:42.877013
Analysis finished2025-03-01 00:39:49.460535
Duration6.58 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

id
Real number (ℝ)

High correlation  Unique 

Distinct5037
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28206886
Minimum4505
Maximum45515581
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.7 KiB
2025-03-01T09:39:49.514644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4505
5-th percentile4173200.2
Q119087342
median30010581
Q339519693
95-th percentile44355959
Maximum45515581
Range45511076
Interquartile range (IQR)20432351

Descriptive statistics

Standard deviation12779971
Coefficient of variation (CV)0.45307983
Kurtosis-0.87737091
Mean28206886
Median Absolute Deviation (MAD)9961534
Skewness-0.49974578
Sum1.4207808 × 1011
Variance1.6332766 × 1014
MonotonicityNot monotonic
2025-03-01T09:39:49.849064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42951790 1
 
< 0.1%
21174388 1
 
< 0.1%
42670833 1
 
< 0.1%
7564825 1
 
< 0.1%
9660334 1
 
< 0.1%
18059552 1
 
< 0.1%
33810838 1
 
< 0.1%
9505429 1
 
< 0.1%
35926860 1
 
< 0.1%
24628815 1
 
< 0.1%
Other values (5027) 5027
99.8%
ValueCountFrequency (%)
4505 1
< 0.1%
7126 1
< 0.1%
9811 1
< 0.1%
10945 1
< 0.1%
12068 1
< 0.1%
12140 1
< 0.1%
22362 1
< 0.1%
24833 1
< 0.1%
25879 1
< 0.1%
28749 1
< 0.1%
ValueCountFrequency (%)
45515581 1
< 0.1%
45515281 1
< 0.1%
45514632 1
< 0.1%
45514091 1
< 0.1%
45513842 1
< 0.1%
45512087 1
< 0.1%
45507485 1
< 0.1%
45504480 1
< 0.1%
45495441 1
< 0.1%
45494553 1
< 0.1%

name
Text

Distinct4913
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size78.7 KiB
2025-03-01T09:39:49.991090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length206
Median length100
Mean length41.284495
Min length2

Characters and Unicode

Total characters207950
Distinct characters314
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4847 ?
Unique (%)96.2%

Sample

1st rowBest deal in the area
2nd rowwhole apartment kitchen **1 bed/bath **living room
3rd rowLogan Square 3 bedrms with parking
4th rowVintage Charm Near Lake
5th rowStudy Oasis
ValueCountFrequency (%)
1699
 
4.9%
in 1172
 
3.4%
chicago 575
 
1.7%
private 561
 
1.6%
park 540
 
1.6%
room 539
 
1.6%
to 528
 
1.5%
the 502
 
1.4%
apartment 482
 
1.4%
bedroom 474
 
1.4%
Other values (3572) 27606
79.6%
2025-03-01T09:39:50.204513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29700
 
14.3%
e 14689
 
7.1%
o 14227
 
6.8%
a 10980
 
5.3%
i 10592
 
5.1%
t 10391
 
5.0%
n 10103
 
4.9%
r 9986
 
4.8%
l 5600
 
2.7%
s 4815
 
2.3%
Other values (304) 86867
41.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 207950
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
29700
 
14.3%
e 14689
 
7.1%
o 14227
 
6.8%
a 10980
 
5.3%
i 10592
 
5.1%
t 10391
 
5.0%
n 10103
 
4.9%
r 9986
 
4.8%
l 5600
 
2.7%
s 4815
 
2.3%
Other values (304) 86867
41.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 207950
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
29700
 
14.3%
e 14689
 
7.1%
o 14227
 
6.8%
a 10980
 
5.3%
i 10592
 
5.1%
t 10391
 
5.0%
n 10103
 
4.9%
r 9986
 
4.8%
l 5600
 
2.7%
s 4815
 
2.3%
Other values (304) 86867
41.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 207950
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
29700
 
14.3%
e 14689
 
7.1%
o 14227
 
6.8%
a 10980
 
5.3%
i 10592
 
5.1%
t 10391
 
5.0%
n 10103
 
4.9%
r 9986
 
4.8%
l 5600
 
2.7%
s 4815
 
2.3%
Other values (304) 86867
41.8%

host_id
Real number (ℝ)

Distinct2924
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99335943
Minimum2140
Maximum3.6790706 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.7 KiB
2025-03-01T09:39:50.268662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2140
5-th percentile1597503
Q117352266
median58773353
Q31.6348161 × 108
95-th percentile3.1407557 × 108
Maximum3.6790706 × 108
Range3.6790492 × 108
Interquartile range (IQR)1.4612934 × 108

Descriptive statistics

Standard deviation1.0003163 × 108
Coefficient of variation (CV)1.0070034
Kurtosis-0.060172575
Mean99335943
Median Absolute Deviation (MAD)50238891
Skewness1.0218632
Sum5.0035515 × 1011
Variance1.0006328 × 1016
MonotonicityNot monotonic
2025-03-01T09:39:50.338765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107434423 156
 
3.1%
3965428 57
 
1.1%
47172572 52
 
1.0%
8534462 39
 
0.8%
359234447 36
 
0.7%
12243051 36
 
0.7%
88566861 36
 
0.7%
9094538 31
 
0.6%
170785489 28
 
0.6%
100782278 27
 
0.5%
Other values (2914) 4539
90.1%
ValueCountFrequency (%)
2140 3
0.1%
2153 2
< 0.1%
2745 1
 
< 0.1%
4434 4
0.1%
5775 1
 
< 0.1%
6162 1
 
< 0.1%
9301 1
 
< 0.1%
11278 1
 
< 0.1%
13014 1
 
< 0.1%
17928 1
 
< 0.1%
ValueCountFrequency (%)
367907062 3
0.1%
366601024 1
 
< 0.1%
366264178 1
 
< 0.1%
365996898 1
 
< 0.1%
365764996 1
 
< 0.1%
365131679 1
 
< 0.1%
365089948 1
 
< 0.1%
363488081 1
 
< 0.1%
363370322 1
 
< 0.1%
362355487 1
 
< 0.1%
Distinct1616
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Memory size78.7 KiB
2025-03-01T09:39:50.450072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length35
Median length32
Mean length6.5215406
Min length1

Characters and Unicode

Total characters32849
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique919 ?
Unique (%)18.2%

Sample

1st rowWilliam
2nd rowJeanne
3rd rowThanh
4th rowBarbara
5th rowAj
ValueCountFrequency (%)
238
 
3.9%
blueground 156
 
2.6%
and 100
 
1.6%
rob 67
 
1.1%
john 57
 
0.9%
michael 56
 
0.9%
alex 54
 
0.9%
zencity 52
 
0.9%
nicole 45
 
0.7%
david 45
 
0.7%
Other values (1594) 5208
85.7%
2025-03-01T09:39:50.626375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 3597
 
11.0%
e 3000
 
9.1%
n 2649
 
8.1%
i 2389
 
7.3%
r 1871
 
5.7%
o 1705
 
5.2%
l 1655
 
5.0%
t 1206
 
3.7%
1049
 
3.2%
d 939
 
2.9%
Other values (65) 12789
38.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32849
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3597
 
11.0%
e 3000
 
9.1%
n 2649
 
8.1%
i 2389
 
7.3%
r 1871
 
5.7%
o 1705
 
5.2%
l 1655
 
5.0%
t 1206
 
3.7%
1049
 
3.2%
d 939
 
2.9%
Other values (65) 12789
38.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32849
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3597
 
11.0%
e 3000
 
9.1%
n 2649
 
8.1%
i 2389
 
7.3%
r 1871
 
5.7%
o 1705
 
5.2%
l 1655
 
5.0%
t 1206
 
3.7%
1049
 
3.2%
d 939
 
2.9%
Other values (65) 12789
38.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32849
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3597
 
11.0%
e 3000
 
9.1%
n 2649
 
8.1%
i 2389
 
7.3%
r 1871
 
5.7%
o 1705
 
5.2%
l 1655
 
5.0%
t 1206
 
3.7%
1049
 
3.2%
d 939
 
2.9%
Other values (65) 12789
38.9%
Distinct77
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size78.7 KiB
2025-03-01T09:39:50.718816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length15
Mean length11.035537
Min length4

Characters and Unicode

Total characters55586
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowGage Park
2nd rowNorth Center
3rd rowLogan Square
4th rowRogers Park
5th rowSouth Shore
ValueCountFrequency (%)
west 1064
 
10.5%
side 1063
 
10.4%
park 973
 
9.6%
near 909
 
8.9%
north 690
 
6.8%
town 568
 
5.6%
lake 422
 
4.1%
view 422
 
4.1%
square 420
 
4.1%
lincoln 328
 
3.2%
Other values (71) 3315
32.6%
2025-03-01T09:39:50.875371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6123
 
11.0%
5137
 
9.2%
r 4586
 
8.3%
a 4181
 
7.5%
o 4167
 
7.5%
t 2873
 
5.2%
n 2714
 
4.9%
i 2442
 
4.4%
d 2087
 
3.8%
S 1797
 
3.2%
Other values (36) 19479
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55586
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6123
 
11.0%
5137
 
9.2%
r 4586
 
8.3%
a 4181
 
7.5%
o 4167
 
7.5%
t 2873
 
5.2%
n 2714
 
4.9%
i 2442
 
4.4%
d 2087
 
3.8%
S 1797
 
3.2%
Other values (36) 19479
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55586
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6123
 
11.0%
5137
 
9.2%
r 4586
 
8.3%
a 4181
 
7.5%
o 4167
 
7.5%
t 2873
 
5.2%
n 2714
 
4.9%
i 2442
 
4.4%
d 2087
 
3.8%
S 1797
 
3.2%
Other values (36) 19479
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55586
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6123
 
11.0%
5137
 
9.2%
r 4586
 
8.3%
a 4181
 
7.5%
o 4167
 
7.5%
t 2873
 
5.2%
n 2714
 
4.9%
i 2442
 
4.4%
d 2087
 
3.8%
S 1797
 
3.2%
Other values (36) 19479
35.0%

latitude
Real number (ℝ)

High correlation 

Distinct4244
Distinct (%)84.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.899259
Minimum41.64736
Maximum42.02251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.7 KiB
2025-03-01T09:39:50.936316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum41.64736
5-th percentile41.782918
Q141.8734
median41.90191
Q341.93976
95-th percentile41.987868
Maximum42.02251
Range0.37515
Interquartile range (IQR)0.06636

Descriptive statistics

Standard deviation0.058928609
Coefficient of variation (CV)0.0014064356
Kurtosis0.81845286
Mean41.899259
Median Absolute Deviation (MAD)0.03435
Skewness-0.73230955
Sum211046.57
Variance0.0034725809
MonotonicityNot monotonic
2025-03-01T09:39:51.004921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.88306 19
 
0.4%
41.89111 15
 
0.3%
41.88608 14
 
0.3%
41.89063 13
 
0.3%
41.88558 11
 
0.2%
41.92819 11
 
0.2%
41.89862 8
 
0.2%
41.88652 7
 
0.1%
41.8989 7
 
0.1%
41.89235 7
 
0.1%
Other values (4234) 4925
97.8%
ValueCountFrequency (%)
41.64736 1
< 0.1%
41.65208 1
< 0.1%
41.65388 1
< 0.1%
41.65578 1
< 0.1%
41.65977 1
< 0.1%
41.68289 1
< 0.1%
41.68612 1
< 0.1%
41.68664 1
< 0.1%
41.6883 1
< 0.1%
41.68906 1
< 0.1%
ValueCountFrequency (%)
42.02251 1
< 0.1%
42.02139 1
< 0.1%
42.02119 1
< 0.1%
42.02105 1
< 0.1%
42.02087 1
< 0.1%
42.02077 1
< 0.1%
42.02042 1
< 0.1%
42.01957 1
< 0.1%
42.01947 1
< 0.1%
42.01926 1
< 0.1%

longitude
Real number (ℝ)

High correlation 

Distinct4052
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-87.663981
Minimum-87.84681
Maximum-87.53752
Zeros0
Zeros (%)0.0%
Negative5037
Negative (%)100.0%
Memory size78.7 KiB
2025-03-01T09:39:51.071925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-87.84681
5-th percentile-87.736012
Q1-87.68671
median-87.66088
Q3-87.63335
95-th percentile-87.604238
Maximum-87.53752
Range0.30929
Interquartile range (IQR)0.05336

Descriptive statistics

Standard deviation0.042619423
Coefficient of variation (CV)-0.000486168
Kurtosis1.2988823
Mean-87.663981
Median Absolute Deviation (MAD)0.027
Skewness-0.67843934
Sum-441563.47
Variance0.0018164152
MonotonicityNot monotonic
2025-03-01T09:39:51.142406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.65131 17
 
0.3%
-87.62205 15
 
0.3%
-87.63422 15
 
0.3%
-87.61903 14
 
0.3%
-87.6525 13
 
0.3%
-87.6257 12
 
0.2%
-87.62797 8
 
0.2%
-87.62472 7
 
0.1%
-87.63337 7
 
0.1%
-87.65159 7
 
0.1%
Other values (4042) 4922
97.7%
ValueCountFrequency (%)
-87.84681 1
< 0.1%
-87.84527 1
< 0.1%
-87.84474 1
< 0.1%
-87.84363 1
< 0.1%
-87.84196 1
< 0.1%
-87.84193 1
< 0.1%
-87.84012 1
< 0.1%
-87.83983 1
< 0.1%
-87.83528 1
< 0.1%
-87.83526 2
< 0.1%
ValueCountFrequency (%)
-87.53752 1
< 0.1%
-87.5379 1
< 0.1%
-87.54496 1
< 0.1%
-87.54557 1
< 0.1%
-87.54593 1
< 0.1%
-87.54595 1
< 0.1%
-87.54596 2
< 0.1%
-87.54603 1
< 0.1%
-87.54615 1
< 0.1%
-87.54775 1
< 0.1%

room_type
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size78.7 KiB
Entire home/apt
3458 
Private room
1448 
Shared room
 
75
Hotel room
 
56

Length

Max length15
Median length15
Mean length14.022434
Min length10

Characters and Unicode

Total characters70631
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowEntire home/apt
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowPrivate room

Common Values

ValueCountFrequency (%)
Entire home/apt 3458
68.7%
Private room 1448
28.7%
Shared room 75
 
1.5%
Hotel room 56
 
1.1%

Length

2025-03-01T09:39:51.207184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-01T09:39:51.266453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
entire 3458
34.3%
home/apt 3458
34.3%
room 1579
15.7%
private 1448
14.4%
shared 75
 
0.7%
hotel 56
 
0.6%

Most occurring characters

ValueCountFrequency (%)
e 8495
12.0%
t 8420
11.9%
o 6672
9.4%
r 6560
9.3%
5037
 
7.1%
m 5037
 
7.1%
a 4981
 
7.1%
i 4906
 
6.9%
h 3533
 
5.0%
n 3458
 
4.9%
Other values (9) 13532
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70631
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8495
12.0%
t 8420
11.9%
o 6672
9.4%
r 6560
9.3%
5037
 
7.1%
m 5037
 
7.1%
a 4981
 
7.1%
i 4906
 
6.9%
h 3533
 
5.0%
n 3458
 
4.9%
Other values (9) 13532
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70631
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8495
12.0%
t 8420
11.9%
o 6672
9.4%
r 6560
9.3%
5037
 
7.1%
m 5037
 
7.1%
a 4981
 
7.1%
i 4906
 
6.9%
h 3533
 
5.0%
n 3458
 
4.9%
Other values (9) 13532
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70631
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8495
12.0%
t 8420
11.9%
o 6672
9.4%
r 6560
9.3%
5037
 
7.1%
m 5037
 
7.1%
a 4981
 
7.1%
i 4906
 
6.9%
h 3533
 
5.0%
n 3458
 
4.9%
Other values (9) 13532
19.2%

price
Real number (ℝ)

Skewed 

Distinct454
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.98015
Minimum0
Maximum9999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size78.7 KiB
2025-03-01T09:39:51.325088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31
Q165
median99
Q3155
95-th percentile399
Maximum9999
Range9999
Interquartile range (IQR)90

Descriptive statistics

Standard deviation364.92497
Coefficient of variation (CV)2.4170394
Kurtosis553.10462
Mean150.98015
Median Absolute Deviation (MAD)43
Skewness21.514007
Sum760487
Variance133170.23
MonotonicityNot monotonic
2025-03-01T09:39:51.391725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 110
 
2.2%
80 101
 
2.0%
75 99
 
2.0%
150 97
 
1.9%
100 88
 
1.7%
65 87
 
1.7%
70 84
 
1.7%
60 77
 
1.5%
90 76
 
1.5%
125 75
 
1.5%
Other values (444) 4143
82.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
10 3
 
0.1%
13 1
 
< 0.1%
14 2
 
< 0.1%
15 6
0.1%
16 2
 
< 0.1%
17 2
 
< 0.1%
18 4
0.1%
19 4
0.1%
20 8
0.2%
ValueCountFrequency (%)
9999 5
0.1%
7000 1
 
< 0.1%
3429 1
 
< 0.1%
3070 1
 
< 0.1%
3000 1
 
< 0.1%
2788 1
 
< 0.1%
1999 1
 
< 0.1%
1921 1
 
< 0.1%
1828 1
 
< 0.1%
1500 3
0.1%

minimum_nights
Real number (ℝ)

Distinct55
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0883462
Minimum1
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.7 KiB
2025-03-01T09:39:51.458281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile31
Maximum500
Range499
Interquartile range (IQR)2

Descriptive statistics

Standard deviation22.371359
Coefficient of variation (CV)2.7658755
Kurtosis160.42948
Mean8.0883462
Median Absolute Deviation (MAD)1
Skewness10.584917
Sum40741
Variance500.4777
MonotonicityNot monotonic
2025-03-01T09:39:51.526211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1630
32.4%
1 1528
30.3%
3 685
13.6%
30 289
 
5.7%
4 135
 
2.7%
31 109
 
2.2%
7 105
 
2.1%
32 87
 
1.7%
5 85
 
1.7%
14 56
 
1.1%
Other values (45) 328
 
6.5%
ValueCountFrequency (%)
1 1528
30.3%
2 1630
32.4%
3 685
13.6%
4 135
 
2.7%
5 85
 
1.7%
6 25
 
0.5%
7 105
 
2.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 33
 
0.7%
ValueCountFrequency (%)
500 1
 
< 0.1%
365 8
0.2%
240 1
 
< 0.1%
210 1
 
< 0.1%
200 1
 
< 0.1%
185 1
 
< 0.1%
182 1
 
< 0.1%
180 7
0.1%
179 1
 
< 0.1%
150 1
 
< 0.1%

number_of_reviews
Real number (ℝ)

High correlation  Zeros 

Distinct308
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.045265
Minimum0
Maximum632
Zeros886
Zeros (%)17.6%
Negative0
Negative (%)0.0%
Memory size78.7 KiB
2025-03-01T09:39:51.591051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median15
Q355
95-th percentile173.2
Maximum632
Range632
Interquartile range (IQR)53

Descriptive statistics

Standard deviation64.638322
Coefficient of variation (CV)1.5373508
Kurtosis11.387585
Mean42.045265
Median Absolute Deviation (MAD)15
Skewness2.8142796
Sum211782
Variance4178.1127
MonotonicityNot monotonic
2025-03-01T09:39:51.657047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 886
 
17.6%
1 326
 
6.5%
2 228
 
4.5%
3 186
 
3.7%
5 118
 
2.3%
4 117
 
2.3%
7 102
 
2.0%
6 83
 
1.6%
9 78
 
1.5%
8 75
 
1.5%
Other values (298) 2838
56.3%
ValueCountFrequency (%)
0 886
17.6%
1 326
 
6.5%
2 228
 
4.5%
3 186
 
3.7%
4 117
 
2.3%
5 118
 
2.3%
6 83
 
1.6%
7 102
 
2.0%
8 75
 
1.5%
9 78
 
1.5%
ValueCountFrequency (%)
632 1
< 0.1%
625 1
< 0.1%
541 1
< 0.1%
511 1
< 0.1%
506 1
< 0.1%
500 1
< 0.1%
499 2
< 0.1%
488 1
< 0.1%
461 1
< 0.1%
442 1
< 0.1%

last_review
Date

Missing 

Distinct680
Distinct (%)16.4%
Missing886
Missing (%)17.6%
Memory size78.7 KiB
Minimum2013-08-18 00:00:00
Maximum2020-09-21 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-03-01T09:39:51.725879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:51.796828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reviews_per_month
Real number (ℝ)

High correlation  Missing 

Distinct627
Distinct (%)15.1%
Missing886
Missing (%)17.6%
Infinite0
Infinite (%)0.0%
Mean1.7346567
Minimum0.02
Maximum32.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.7 KiB
2025-03-01T09:39:51.861722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.09
Q10.43
median1.24
Q32.56
95-th percentile4.9
Maximum32.43
Range32.41
Interquartile range (IQR)2.13

Descriptive statistics

Standard deviation1.7247189
Coefficient of variation (CV)0.994271
Kurtosis27.811089
Mean1.7346567
Median Absolute Deviation (MAD)0.94
Skewness2.8437583
Sum7200.56
Variance2.9746552
MonotonicityNot monotonic
2025-03-01T09:39:51.930779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 59
 
1.2%
0.17 49
 
1.0%
0.14 43
 
0.9%
0.08 41
 
0.8%
0.16 36
 
0.7%
0.03 33
 
0.7%
0.09 33
 
0.7%
0.12 32
 
0.6%
0.22 32
 
0.6%
0.19 29
 
0.6%
Other values (617) 3764
74.7%
(Missing) 886
 
17.6%
ValueCountFrequency (%)
0.02 12
 
0.2%
0.03 33
0.7%
0.04 27
0.5%
0.05 20
0.4%
0.06 23
0.5%
0.07 22
0.4%
0.08 41
0.8%
0.09 33
0.7%
0.1 23
0.5%
0.11 28
0.6%
ValueCountFrequency (%)
32.43 1
< 0.1%
16.93 1
< 0.1%
11.69 1
< 0.1%
11.54 1
< 0.1%
11.15 1
< 0.1%
11.07 1
< 0.1%
10.97 1
< 0.1%
10.86 1
< 0.1%
10.51 1
< 0.1%
9.52 1
< 0.1%
Distinct33
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.482629
Minimum1
Maximum205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.7 KiB
2025-03-01T09:39:51.992816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q37
95-th percentile62
Maximum205
Range204
Interquartile range (IQR)6

Descriptive statistics

Standard deviation36.67142
Coefficient of variation (CV)2.7199014
Kurtosis20.207855
Mean13.482629
Median Absolute Deviation (MAD)1
Skewness4.4866916
Sum67912
Variance1344.793
MonotonicityNot monotonic
2025-03-01T09:39:52.055842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1 2116
42.0%
2 676
 
13.4%
3 357
 
7.1%
4 297
 
5.9%
205 156
 
3.1%
5 149
 
3.0%
6 102
 
2.0%
7 101
 
2.0%
9 94
 
1.9%
8 78
 
1.5%
Other values (23) 911
18.1%
ValueCountFrequency (%)
1 2116
42.0%
2 676
 
13.4%
3 357
 
7.1%
4 297
 
5.9%
5 149
 
3.0%
6 102
 
2.0%
7 101
 
2.0%
8 78
 
1.5%
9 94
 
1.9%
10 58
 
1.2%
ValueCountFrequency (%)
205 156
3.1%
73 57
 
1.1%
62 52
 
1.0%
47 75
1.5%
45 36
 
0.7%
44 36
 
0.7%
37 31
 
0.6%
31 52
 
1.0%
30 24
 
0.5%
28 27
 
0.5%

availability_365
Real number (ℝ)

Zeros 

Distinct361
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.55906
Minimum0
Maximum365
Zeros948
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size78.7 KiB
2025-03-01T09:39:52.122144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q134
median159
Q3329
95-th percentile365
Maximum365
Range365
Interquartile range (IQR)295

Descriptive statistics

Standard deviation138.89201
Coefficient of variation (CV)0.8002579
Kurtosis-1.5528595
Mean173.55906
Median Absolute Deviation (MAD)152
Skewness0.12234251
Sum874217
Variance19290.991
MonotonicityNot monotonic
2025-03-01T09:39:52.190670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 948
 
18.8%
365 293
 
5.8%
364 127
 
2.5%
90 87
 
1.7%
89 75
 
1.5%
263 71
 
1.4%
179 63
 
1.3%
355 57
 
1.1%
180 57
 
1.1%
363 52
 
1.0%
Other values (351) 3207
63.7%
ValueCountFrequency (%)
0 948
18.8%
1 43
 
0.9%
2 10
 
0.2%
3 22
 
0.4%
4 15
 
0.3%
5 8
 
0.2%
6 10
 
0.2%
7 12
 
0.2%
8 4
 
0.1%
9 7
 
0.1%
ValueCountFrequency (%)
365 293
5.8%
364 127
2.5%
363 52
 
1.0%
362 50
 
1.0%
361 29
 
0.6%
360 49
 
1.0%
359 36
 
0.7%
358 29
 
0.6%
357 21
 
0.4%
356 22
 
0.4%

Interactions

2025-03-01T09:39:48.658993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:43.209650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:43.869194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:44.436011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:45.003442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:45.721389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:46.257545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:46.791891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:47.542111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:48.087154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:48.710709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:43.262654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:43.921248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:44.489530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:45.055315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:45.769505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:46.306584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:46.846960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:47.591705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:48.139768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:48.767050image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:43.315951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:43.977354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:44.547268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:45.112667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:45.824049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:46.361806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:46.901790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:47.648530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:48.198825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:48.824512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:43.370813image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:44.035957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:44.604771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:45.330827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:45.877243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:46.415791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:46.958947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:47.703623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:48.258891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:48.879856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:43.426993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:44.093764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:44.662421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:45.385517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:45.930949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:46.470736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:47.014685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:47.759563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:48.316713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:48.938383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:43.476567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:44.148404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:44.717191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:45.440098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:45.980258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:46.520982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:47.067164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:47.811026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:48.372392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:48.995127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:43.528431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:44.204067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:44.771778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:45.493510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:46.031643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:46.570832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:47.117142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:47.866555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:48.426976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:49.050045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:43.709981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:44.262115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:44.829517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:45.549510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:46.084097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:46.624310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:47.375252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:47.920842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:48.484365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:49.104028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:43.760891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:44.319042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:44.885135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:45.605417image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:46.136250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:46.680776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:47.429411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:47.974304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:48.541556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:49.162292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:43.816549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:44.379159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:44.946398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:45.664021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:46.190660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:46.738089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:47.486231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:48.032625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-03-01T09:39:48.601489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-03-01T09:39:52.245320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
availability_365calculated_host_listings_counthost_ididlatitudelongitudeminimum_nightsnumber_of_reviewspricereviews_per_monthroom_type
availability_3651.0000.277-0.0160.004-0.0700.0780.179-0.0290.1150.0180.083
calculated_host_listings_count0.2771.0000.0330.227-0.1850.2950.152-0.2120.038-0.0370.145
host_id-0.0160.0331.0000.471-0.1260.054-0.098-0.244-0.0090.0820.107
id0.0040.2270.4711.000-0.1460.1200.052-0.6400.005-0.0050.101
latitude-0.070-0.185-0.126-0.1461.000-0.522-0.0890.1290.0820.0320.151
longitude0.0780.2950.0540.120-0.5221.0000.137-0.1880.188-0.0880.105
minimum_nights0.1790.152-0.0980.052-0.0890.1371.000-0.2740.135-0.2600.054
number_of_reviews-0.029-0.212-0.244-0.6400.129-0.188-0.2741.000-0.1090.7910.000
price0.1150.038-0.0090.0050.0820.1880.135-0.1091.000-0.0890.103
reviews_per_month0.018-0.0370.082-0.0050.032-0.088-0.2600.791-0.0891.0000.101
room_type0.0830.1450.1070.1010.1510.1050.0540.0000.1030.1011.000

Missing values

2025-03-01T09:39:49.249305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-01T09:39:49.328809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-01T09:39:49.426374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idnamehost_idhost_nameneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
193021174388Best deal in the area43392136WilliamGage Park41.79359-87.70345Private room4611622020-09-114.503364
558842670833whole apartment kitchen **1 bed/bath **living room110299543JeanneNorth Center41.95803-87.69214Entire home/apt12020NaNNaN181
5997564825Logan Square 3 bedrms with parking6427776ThanhLogan Square41.92654-87.72505Entire home/apt14621002019-12-011.601180
7789660334Vintage Charm Near Lake11520899BarbaraRogers Park42.00451-87.66203Entire home/apt1357672020-08-031.17173
150918059552Study Oasis124307753AjSouth Shore41.76867-87.56926Private room221412020-09-061.062319
3660338108382F-2BR Apt in Bridgeport along Archer Ave.\n聚华坊150847041HenryBridgeport41.84030-87.65903Entire home/apt1182202020-09-071.20224
7669505429Roscoe Village Inn|Walk to Chicago's Wrigley Field49260140KimberlyNorth Center41.94629-87.68011Entire home/apt179322016-06-120.0431
401835926860Lux River North Studio w/ Gym, W/D, nr. Magnificent Mile, by Blueground107434423BluegroundNear North Side41.89502-87.62791Entire home/apt130300NaNNaN205344
241624628815Chicago's Balloon62447270FernandaBridgeport41.83363-87.65166Private room443232020-08-030.79189
442938192546Dog friendly, entire apt in trendy Logan Square.58057341BrettLogan Square41.92595-87.69130Entire home/apt110372019-10-280.5510
idnamehost_idhost_nameneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
270926776933Room D (Queen size bed)40832295Shannon & CarmenBelmont Cragin41.92027-87.77840Private room115262019-08-040.234365
341832051074Character 2 Bedroom & Balcony-Heart of Chic Center62066342RafaelloNear North Side41.89163-87.63800Entire home/apt1091200NaNNaN5365
108213798441Beautiful 1-bedroom, 1-bathroom Bucktown Apartment81125437JulieLogan Square41.92309-87.68304Entire home/apt7021112020-09-132.17181
418536910970Splendid Skyscraper in the Heart of Chicago260807399JohnNear West Side41.88340-87.64307Entire home/apt400232019-11-100.2360
135616054326Brand New Logan Square Private Floor with Laundry!20852412TawniAvondale41.93312-87.70809Private room1391352020-02-170.871109
1872320027Carriage House in Wicker Park11848299AntonWest Town41.90669-87.68283Entire home/apt11421582019-09-161.9710
322830171391Classic Chicago (entire ) 3 bed Row HousePARK FREE25715675JoyceEast Garfield Park41.87980-87.70136Entire home/apt794892020-08-284.18319
2192227707793 Level Coach House in Roscoe Village with Patio!30320286AqueelNorth Center41.94383-87.68014Entire home/apt1284812020-09-052.811344
6387937969CHICAGO WRIGLEYVILLE CUBS HEADQUARTERS5547803AnneLake View41.95045-87.65558Entire home/apt125132019-05-120.0610
569042951790❤️ Low Prices! Stylish 2BR Condo in Logan Square!341917367JoanLogan Square41.92068-87.71599Entire home/apt106422020-09-192.00885